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In distributed fusion architecture, processed information in the form of tracked object data is available instead of raw sensor data. Ignoring the cross-correlation in distributed systems by employing Kalman filter in general leads to inconsistent results. Covariance intersection, on the other hand provide conservative results by overestimating the intersection of individual covariances. In this paper,...
Some crucial challenges of estimation over sensor networks are reaching consensus on the estimates of different systems in the network and separating the mutual information of two estimates from their exclusive information. Current fusion methods of two estimates tend to bypass the mutual information and directly optimize the fused estimate. Moreover, both the mean and covariance of the fused estimate...
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